artificial intelligence trends in 2024

As we know AI is evolving faster than any other technology every industry, organization, and company is turning to AI technology for better customer support by 2024 with artificial intelligence changing the world.

Smaller language model

Small Language Models (SLMs) are a type of AI that focus on specific applications and tasks, SLMs are much smaller in size (billions of parameters) compared to LLMs which have billions of parameters unlike their bigger cousins, Large Language Models (LLMs) . . . . . This makes them faster and requires less computing. SLMs are trained for specific tasks, making them more accurate and effective in those areas. They can be optimized for applications such as chatbots, text completion, language translation, etc. Due to their lower resource requirements, SLMs are more accessible. This allows small businesses and even individuals to take advantage of AI technologies without significant investment. SLMs shine in applications where speed and productivity are critical. They are ideal for on-tool accessories, real-time data analysis, and edge-counting scenarios.

Smaller Language Models Example

DistilBERT: DistilBERT is based on the powerful large language model (LLM) BERT, but it’s been “compressed” using knowledge distillation. This makes it much smaller and faster than BERT while retaining most of its capabilities.

MobileBERT: MobileBERT is another exciting advancement in smaller language models (SLMs), specifically designed for resource-limited devices like smartphones and other mobile platforms.

Powerful virtual agents

As you know AI is developing much faster by 2024. All powerful virtual agents are about intelligent assistance and extensive capabilities. These representatives are not limited to hearing your voice. They are multifaceted, processing information from text, images and even video. Imagine an agent answering your question based on an image you showed them, or creating a report that combines your notes with web data. Powerful virtual agents use state-of-the-art AI such as generative AI to contribute significantly. This could mean a representative writing emails your way, translating languages during video calls, or even summarizing complex documents for you. These proxies are machine learning devices that adapt to your needs and preferences. Imagine an agent anticipating your plan, actively suggesting relevant information, or even helping you brainstorm ideas.

Powerful Virtual Agents Example

OpenAI’s ChatGPT-4.5:

Building on the success of GPT-4, ChatGPT-4.5 offers even more refined conversational abilities, generative AI better contextual understanding, and enhanced ability to handle complex queries. It can engage in more natural and human-like conversations. Widely integrated into various platforms, from customer service chatbots to personal assistant apps, and supports a broad range of languages. Improved memory to maintain context over longer conversations, better handling of ambiguous queries, and integration with real-time data for up-to-date information.

Google Assistant:

Leveraging advancements in Google’s AI research, Google Assistant provides robust voice and text interaction, seamless integration with Google’s ecosystem, and superior contextual understanding. Deep integration with Google services (Gmail, Calendar, Maps, etc.) and third-party applications. It’s also embedded in a wide range of smart home devices. Multimodal interactions (voice, text, and visual), proactive assistance (suggesting actions based on context and habits), and improved multilingual support.

Amazon Alexa:

Alexa has evolved with enhanced natural language understanding, better dialogue management, and more personalized responses. Extensive compatibility with smart home devices, third-party apps, and Amazon services (shopping, music, etc.).Enhanced routines and automation, more natural and expressive voice options, and integration with Amazon’s Echo devices for a seamless smart home experience.

Apple Siri:

Siri has seen improvements in understanding context, maintaining conversation threads, and integrating more deeply with iOS and macOS functionalities. Tight integration with Apple’s ecosystem, providing seamless functionality across iPhone, iPad, Apple Watch, and Mac. Improved voice recognition, more personalized assistance based on user habits and preferences, and better handling of complex commands.

Embodied AI

Embodied AI is a sub-field of AI that focuses on creating intelligent objects that interact with the physical world through physical bodies, such as robots Embodied AI aims to differentiate traditional functional AI in digital form, and between real-world applications that do not seem to learn from data, embodied AI learns by interacting with the physical environment using sensors (cameras, touch sensors) and actuators (motors) operate so Imagine a robot physically orienting itself in a room and adjusting its motion. In tangible terms, AI has the potential to change things. Imagine robots performing surgeries, helping in disaster areas, or even helping with chores in our homes.

Robotics: Embodied AI is It is contributing to the development of agile, scalable, and intelligent robots. This could lead to the development of robotic objects capable of performing complex tasks in unstructured environments.
Human-Robot Interaction: As embodied AI agents become more sophisticated, they will be able to interact with humans in more natural ways. This could lead to robots that can become companions, caregivers, or even co-workers.
Autonomous Systems: Embodied AI is playing a role in the development of autonomous systems, such as self-driving cars and drones. These systems need to be able to perceive and react to their environment in real time.

Challenges and Considerations:

Developing embodied AI is complex and there are several challenges to overcome. These include:

Safe Interaction: Ensuring that embodied AI agents can interact with the physical world safely is crucial.
Sensorimotor Integration: Embodied AI agents need to be able to integrate information from their sensors to control their movements effectively.
Limited Resources: Robots and other physical embodiments often have limited computational power and energy resources.

The Future of Embodied AI:

Embodied AI is a rapidly evolving field with the potential to significantly impact our lives. As research progresses, we can expect to see even more sophisticated embodied AI agents emerge, transforming the way we interact with the physical world.

Embodied AI examples:

Spot by Boston Dynamics: This dog-like robot is a great example of embodied AI. Spot uses sensors like cameras and LiDAR to navigate its environment. It can also be equipped with other sensors for specific tasks. Imagine Spot being used on a construction site to inspect hard-to-reach areas or even help with search and rescue missions in dangerous terrain.

Sophia the Robot by Hanson Robotics: While Sophia is not designed for physical tasks, Sophia is a humanoid robot that demonstrates some of the capabilities of embodied AI in human-robot interaction Sophia can use facial expressions and talk, making it a tool a valuable for research in social robotics. Imagine a robot like Sophia being used in customer service or education, creating a more natural and enjoyable experience. the capabilities of embodied AI in human-robot interaction. Sophia can use facial expressions and carry on conversations, making her a valuable tool for research in social robotics. Imagine robots like Sophia being used in customer service or education, providing a more natural and engaging experience.